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Learning Objective 1
• Define, clearly, the problem with healthcare’s current state of predictive modeling implementations and
how they often fail to support clinical workflows and describe the CAPE framework for how to bring
multiple predictive models into a single prescriptive engine
Learning Objective 2
• Describe an inventory of key patient outcomes to predict and how to achieve a high accuracy for
prediction including both retrospective and prospective validation processes
Learning Objective 3
• Demonstrate the importance of tightly integrated predictive models into the EHR using real-time
processing via the Predictive Model Markup Language (PMML) including implications for displaying the
results and risk factors of a model to front-line clinicians
Learning Objective 4.
• Discuss the implications of a learning health system and how CAPE can help to achieve a better
understanding of the impactability of patient populations based on multiple risk models and propose
specific intervention bundles catered to the needs of that population
Learning Objective 5.
• Discuss the key cultural implications that an integrated predictive engine is able to facilitate and how it
can enable the care team to improve patient outcomes while lowering costs
Learning Objectives